Modern wireless data communications systems may utilize the benefits of multiple transmit and receive antennas to improve both range and data communications bandwidth. In particular, Multiple Input Multiple Output (MIMO) systems can utilize the spatio-temporal properties of the RF channel to transmit parallel but independent streams of data, greatly increasing the amount of data that can be transferred from a transmitter to a receiver. MIMO systems are therefore seeing widespread use in high-speed wireless digital transmission systems.
MIMO systems take advantage of the fact that radio frequency (RF) environments may contain large numbers of reflective and diffractive objects, the effect of which is to impose multiple propagation modes on the RF channel existing between a transmitter and a receiver. The number of available propagation modes is determined by the number of antennas at the transmitter and receiver. Each individual propagation mode may convey an independent, uncorrelated RF signal, which may then be distinguished, identified and extracted at the receiver. A MIMO transmitter may therefore split a single stream of digital data into independent parallel streams and transmit each stream on a different propagation mode as a separate RF signal. Given an adequate number of receive antennas, and knowledge of the channel propagation modes, a MIMO receiver may separate and decode the parallel streams of data and may combine them to regenerate the original stream of digital data. The capacity of a MIMO system is therefore a multiple of the capacity of a conventional Single Input Single Output (SISO) system operating over the same RF channel.
A MIMO RF wireless channel with N transmit and M receive antennas may be represented as an N×M complex matrix, referred to as a H or channel matrix, with each matrix coefficient representing the transfer function between an individual pair of transmit and receive antennas. As the number of available propagation modes is the lesser of the number of transmit and receive antennas, it may be usual to set N=M for an equal number of antennas on both sides, making the maximum number of parallel data streams that may be transmitted equal to N.
An exemplary MIMO RF wireless communication system may contain a transmitter that may accept a stream of digital transmit data which may then be split into parallel streams, processed using a space-time coding method, modulated, and eventually transformed to RF signals that may be transmitted on the N transmit antennas. These signals may then traverse the RF channel represented by the channel matrix, and then may arrive at the N receive antennas of a MIMO receiver. The receiver may then convert and process these signals, and may further use a MIMO equalizer to remove the effects of the channel matrix upon the transmitted signals and may recover the original space-time coded signal. The equalized signal may then be passed to a demodulator and MIMO decoder which may invert the space-time coding to regain the separate streams of data signals, which may then be reconstituted back into the original digital transmit data.
It is apparent that a MIMO equalizer in a wireless receiver may play a key role in reception by compensating for the effects of the RF channel and allowing the extraction of individual signal streams that may have been impressed into the various propagation modes of the channel matrix. As the equalization coefficients utilized by the MIMO equalizer may be dependent on the channel matrix, which may vary significantly by physical location and time, the coefficients of the channel matrix may have to be accurately estimated at the receiver in order to receive the data. This process is known as channel estimation. Once these channel estimates have been determined, they may be used in turn to calculate the coefficients used by the MIMO equalizer. A poor channel estimate may make it difficult or impossible to correctly separate the data streams, leading to a much higher error rate for the recovered data streams at the same signal level. Therefore, accurate channel estimation may be extremely important for MIMO systems.
Estimation of the channel matrix may be accomplished by using special known training sequences (e.g., preamble sequences or pilot signals), which may be extracted from the transmitted signal, which may for instance be an IEEE 802.11 Wireless Local Area Network (WLAN) packet stream or a 3GPP Long Term Evolution (LTE) frame stream. For example, the MIMO reception function may first determine the value of the channel matrix H using known symbols in preamble and/or pilot signals transmitted by the MIMO transmitter, and may use these to calculate the complex equalization matrix that may then be used to recover the payload. However, the number of these training sequences or pilots may be relatively small compared to the data signals, which may adversely affect the accuracy of channel estimation. As an alternative, channel estimation may be accomplished under certain conditions using the actual packet data payload itself, which may greatly increase the amount of signals available for improving the quality of the estimates. Various approaches to achieve this are discussed in co-pending U.S. patent application Ser. No. 61/900,973, herein incorporated by reference. As such, it is assumed that the details of different methods of generating a channel estimate, or the coefficients of a channel equalization matrix, or both, are available to a person skilled in the art and need not be discussed further herein. It should also be understood that channel estimation may directly comprise the calculation of the equalization matrix, omitting the calculation of the intermediate H matrix as it may not be strictly necessary for wireless reception.
Different methods of calculating the equalization matrix are known, which may range from simple zero-forcing to more complex systems such as sphere decoding. However, due to distortion in the RF transmitter and noise in the RF channel (as well as signal noise in both RF transmitter and receiver), it may not be possible to exactly calculate the channel matrix H or the equivalent equalization matrix. Unfortunately, inaccuracies in the calculations may increase the probability of errors in the decoded receive data. It may therefore be essential to make the channel estimates as accurate as possible in the face of noise and distortion.
These errors and variations may particularly impact higher-order modulations, such as Orthogonal Frequency Division Multiplexing (OFDM) with 64 and 256 Quadrature Amplitude Modulation (QAM) modulations; higher bandwidth transmissions (such as 80 MHz and 160 MHz channel bandwidths) where the channel may not be frequency-flat over its entire range; and may also adversely affect larger packet sizes where the ratio of training signals to data signals is lower. This may make the system more susceptible to inaccuracies in the estimated channel. Another significant issue is that the channel characteristics and therefore the channel estimate may become inaccurate over time due to changing propagation conditions: for example, if the MIMO transmitter and receiver move with respect to each other, or if the RF propagation environment itself changes. This may induce errors in the decoded receive data stream, as the equalization matrix coefficients may no longer be accurate.
As noted in the above-referenced co-pending U.S. patent application, a possible method of reducing the impact of noise and distortion is to use the training fields in the preamble to derive an initial channel estimate and then use the information in the data payload of the packet to refine the estimate. However, this may be primarily applicable to long data packets, as short data packets may not contain enough data symbols to produce a usable channel estimate from the random bit patterns present in the payload. Another possible method, also noted in the above-referenced co-pending application, is to increase accuracy by averaging channel estimates over multiple successive packets. However, this approach may require that the channel be largely invariant over time, and may also require some method of distinguishing between packets from different sources that are received over different RF channels. Again, this latter approach may be principally applicable to long data packets, as the time required to determine where the packet is coming from and the approximate nature of the RF channel may be too long to enable it to be applied to short packets.
In general, if the average packet data payload length is long, then it may be possible to generate a good channel estimate from a few consecutive packets. For a short average payload length, however, a large number of closely spaced packets may need to be processed to obtain a reasonably accurate channel estimate. Thus the initial data packets in a burst of data do not benefit from techniques to improve channel estimation, unless they are buffered for later processing. However, buffering may incur significant latency, which may not be tolerated in the system. Alternative techniques to accumulate channel estimates over a number of short packets may suffer from the issue that it is difficult to determine when the channel has actually changed and the estimates need to be recalculated from scratch. Further, the rapid storage, lookup, matching and retrieval of channel estimates for a large number of RF sources transmitting short packets to a single receiver at high rates may be computationally prohibitive.
It may be apparent from the foregoing discussion that accurate and efficient channel estimation is important for high-bandwidth wireless communication. It may further be apparent that more advanced technologies such as OFDM and MIMO may greatly increase the need for accurate and reliable channel estimation and equalization. However, the initial channel estimates that may be obtained from the short training sequences embedded in the preamble may not be adequate, and averaging estimates over a number of short frames may become inaccurate if the channel changes substantially over time.
There is hence a need for improved MIMO wireless channel estimation and equalization systems and methods. A system that can quickly identify when a stored channel estimate is usable for an incoming packet may be desirable. Further, it may be preferred in such a system to be able to achieve this with little computational overhead, for example to allow its implementation using existing hardware technologies. Such a system may preferably be applicable to communications involving long bursts of short frames received from multiple different wireless transmitters. Finally, it may be desirable for such a system to detect and adapt to changing RF channel conditions.